v1.14.2

changelog.md

@@ -1,5 +1,15 @@
 # Changelog
 
+## 2026-01-19 - 1.14.2 - fix(readme)
+update README to document Nanonets-OCR2-3B (replaces Nanonets-OCR-s), adjust VRAM and context defaults, expand feature docs, and update examples/test command
+
+- Renamed Nanonets-OCR-s -> Nanonets-OCR2-3B throughout README and examples
+- Updated Nanonets VRAM guidance from ~10GB to ~12-16GB and documented 30K context
+- Changed documented MAX_MODEL_LEN default from 8192 to 30000
+- Updated example model identifiers (model strings and curl/example snippets) to nanonets/Nanonets-OCR2-3B
+- Added MiniCPM and Qwen feature bullets (multilingual, multi-image, flowchart support, expanded context notes)
+- Replaced README test command from ./test-images.sh to pnpm test
+
 ## 2026-01-19 - 1.14.1 - fix(extraction)
 improve JSON extraction prompts and model options for invoice and bank statement tests
 

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.14.1",
+  "version": "1.14.2",
   "type": "module",
   "private": false,
   "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",

readme.md

@@ -2,7 +2,7 @@
 
 Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration—**no cloud API keys required**.
 
-> 🔥 **Three VLMs, one registry.** From lightweight document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
+> 🔥 **Three VLMs, one registry.** From high-performance document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
 
 ## Issue Reporting and Security
 
@@ -15,7 +15,7 @@ For reporting bugs, issues, or security vulnerabilities, please visit [community
 | Model | Parameters | Best For | API | Port | VRAM |
 |-------|-----------|----------|-----|------|------|
 | **MiniCPM-V 4.5** | 8B | General vision understanding, multi-image analysis | Ollama-compatible | 11434 | ~9GB |
-| **Nanonets-OCR-s** | ~4B | Document OCR with semantic markdown output | OpenAI-compatible | 8000 | ~10GB |
+| **Nanonets-OCR2-3B** | ~3B | Document OCR with semantic markdown, LaTeX, flowcharts | OpenAI-compatible | 8000 | ~12-16GB |
 | **Qwen3-VL-30B** | 30B (A3B) | Advanced visual agents, code generation from images | Ollama-compatible | 11434 | ~20GB |
 
 ---
@@ -29,7 +29,7 @@ code.foss.global/host.today/ht-docker-ai:<tag>
 | Tag | Model | Runtime | Port | VRAM |
 |-----|-------|---------|------|------|
 | `minicpm45v` / `latest` | MiniCPM-V 4.5 | Ollama | 11434 | ~9GB |
-| `nanonets-ocr` | Nanonets-OCR-s | vLLM | 8000 | ~10GB |
+| `nanonets-ocr` | Nanonets-OCR2-3B | vLLM | 8000 | ~12-16GB |
 | `qwen3vl` | Qwen3-VL-30B-A3B | Ollama | 11434 | ~20GB |
 
 ---
@@ -38,6 +38,13 @@ code.foss.global/host.today/ht-docker-ai:<tag>
 
 A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across **30+ languages**.
 
+### ✨ Key Features
+
+- 🌍 **Multilingual:** 30+ languages supported
+- 🖼️ **Multi-image:** Analyze multiple images in one request
+- 📊 **Versatile:** Charts, documents, photos, diagrams
+- ⚡ **Efficient:** Runs on consumer GPUs (9GB VRAM)
+
 ### Quick Start
 
 ```bash
@@ -83,21 +90,22 @@ curl http://localhost:11434/api/chat -d '{
 
 | Mode | VRAM Required |
 |------|---------------|
-| int4 quantized | 9GB |
+| int4 quantized | ~9GB |
-| Full precision (bf16) | 18GB |
+| Full precision (bf16) | ~18GB |
 
 ---
 
-## 🔍 Nanonets-OCR-s
+## 🔍 Nanonets-OCR2-3B
 
-A **Qwen2.5-VL-3B** model fine-tuned specifically for document OCR. Outputs structured markdown with semantic HTML tags—perfect for preserving document structure.
+The **latest Nanonets document OCR model** (October 2025 release)—based on Qwen2.5-VL-3B, fine-tuned specifically for document extraction with significant improvements over the original OCR-s.
 
-### Key Features
+### ✨ Key Features
 
-- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, watermarks/page numbers → tagged
+- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, flowcharts → structured markup
 - 🌍 **Multilingual:** Inherits Qwen's broad language support
-- ⚡ **Efficient:** ~10GB VRAM, runs great on consumer GPUs
+- 📄 **30K context:** Handle large, multi-page documents
 - 🔌 **OpenAI-compatible:** Drop-in replacement for existing pipelines
+- 🎯 **Improved accuracy:** Better semantic tagging and LaTeX equation extraction vs. OCR-s
 
 ### Quick Start
 
@@ -116,7 +124,7 @@ docker run -d \
 curl http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "nanonets/Nanonets-OCR-s",
+    "model": "nanonets/Nanonets-OCR2-3B",
     "messages": [{
       "role": "user",
       "content": [
@@ -131,7 +139,7 @@ curl http://localhost:8000/v1/chat/completions \
 
 ### Output Format
 
-Nanonets-OCR-s returns markdown with semantic tags:
+Nanonets-OCR2-3B returns markdown with semantic tags:
 
 | Element | Output Format |
 |---------|---------------|
@@ -140,13 +148,14 @@ Nanonets-OCR-s returns markdown with semantic tags:
 | Images | `<img>description</img>` |
 | Watermarks | `<watermark>OFFICIAL COPY</watermark>` |
 | Page numbers | `<page_number>14</page_number>` |
+| Flowcharts | Structured markup |
 
-### Performance
+### Hardware Requirements
 
-| Metric | Value |
+| Config | VRAM |
-|--------|-------|
+|--------|------|
-| Speed | 3–8 seconds per page |
+| 30K context (default) | ~12-16GB |
-| VRAM | ~10GB |
+| Speed | ~3-8 seconds per page |
 
 ---
 
@@ -154,7 +163,7 @@ Nanonets-OCR-s returns markdown with semantic tags:
 
 The **most powerful** Qwen vision model—30B parameters with 3B active (MoE architecture). Handles complex visual reasoning, code generation from screenshots, and visual agent capabilities.
 
-### Key Features
+### ✨ Key Features
 
 - 🚀 **256K context** (expandable to 1M tokens!)
 - 🤖 **Visual agent capabilities** — can plan and execute multi-step tasks
@@ -204,7 +213,6 @@ curl http://localhost:11434/api/chat -d '{
 Run multiple VLMs together for maximum flexibility:
 
 ```yaml
-version: '3.8'
 services:
   # General vision tasks
   minicpm:
@@ -259,10 +267,10 @@ volumes:
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `MODEL_NAME` | `nanonets/Nanonets-OCR-s` | HuggingFace model ID |
+| `MODEL_NAME` | `nanonets/Nanonets-OCR2-3B` | HuggingFace model ID |
 | `HOST` | `0.0.0.0` | API bind address |
 | `PORT` | `8000` | API port |
-| `MAX_MODEL_LEN` | `8192` | Maximum sequence length |
+| `MAX_MODEL_LEN` | `30000` | Maximum sequence length |
 | `GPU_MEMORY_UTILIZATION` | `0.9` | GPU memory usage (0-1) |
 
 ---
@@ -283,7 +291,7 @@ This dual-VLM approach catches extraction errors that single models miss.
 ### Why Multi-Model Works
 
 - **Different architectures:** Independent models cross-validate each other
-- **Specialized strengths:** Nanonets-OCR-s excels at document structure; MiniCPM-V handles general vision
+- **Specialized strengths:** Nanonets-OCR2-3B excels at document structure; MiniCPM-V handles general vision
 - **Native processing:** All VLMs see original images—no intermediate structure loss
 
 ### Model Selection Guide
@@ -291,10 +299,11 @@ This dual-VLM approach catches extraction errors that single models miss.
 | Task | Recommended Model |
 |------|-------------------|
 | General image understanding | MiniCPM-V 4.5 |
-| Document OCR with structure preservation | Nanonets-OCR-s |
+| Document OCR with structure preservation | Nanonets-OCR2-3B |
 | Complex visual reasoning / code generation | Qwen3-VL-30B |
 | Multi-image analysis | MiniCPM-V 4.5 |
 | Visual agent tasks | Qwen3-VL-30B |
+| Large documents (30K+ tokens) | Nanonets-OCR2-3B |
 
 ---
 
@@ -309,7 +318,7 @@ cd ht-docker-ai
 ./build-images.sh
 
 # Run tests
-./test-images.sh
+pnpm test
 ```
 
 ---